Abstract
Tissue-level brain responses to sport-related head impacts may be stronger predictors of brain injury risk than head kinematics alone. Despite the importance of accurate impact response estimation, the influence of head morphological variations has not been properly considered due to the limited sizes and shapes of existing computational head models. In this study, we developed 101 subject-specific finite element (FE) head-brain models based on CT scans and a parametric modeling approach to estimate tissue-level brain impact responses (maximal principal strain, MPS) under three head impact conditions. Principal component analysis (PCA) was used to quantify the geometric variations, with statistically significant PCs then selected to predict MPS using a stepwise linear regression model. High adjusted R2 values (0.6–0.9) were achieved in the regression model, suggesting a good model predictability. Brain volume explained the largest variance of 51.3%, and it was highly correlated with MPS, indicating a significant size effect on brain impact responses. This is the first modeling study to systematically consider the influence of morphological variations in the inner skull and scalp on brain tissue impact response.
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